This review critically examines the current landscape of protein biophysics, highlighting a significant epistemological divide emerging from the rapid advancement of predictive algorithms compared to the slower pace of experimental validation. While artificial intelligence models and inferential statistical methods can generate millions of in silico protein interactions, experimental biophysics remains limited by physiological and technical challenges, leading to a scarcity of confirmed, functional data. This imbalance creates an environment where hypotheses and large-scale interactome networks proliferate based on unverified interactions. This fosters a false sense of causal understanding, potential misdirection in therapeutic development, and overinterpreting omics datasets. The challenge becomes even more urgent with complex, multifactorial diseases such as cancer and viral infections. Recognizing this subtle but crucial disparity is essential for researchers in protein biophysics to navigate the evolving landscape effectively and to focus validation efforts that support meaningful scientific and clinical progress. The review emphasizes the critical need for rigorous experimental validation alongside rapid computational advances. It highlights key challenges, including complexities in protein folding, dynamics, membrane interactions, and quantum effects. It discusses emerging technologies such as AI-based structure prediction (e.g., AlphaFold), cryo-electron microscopy, and integrated biophysical approaches. The importance of multidisciplinary collaboration and orthogonal validation methods is underscored to enhance the reliability of protein interaction data. While technological advances promise to deepen our understanding of protein functions and their roles in health and disease, the review advocates for cautious integration of predictive models with meticulous experimental verification, ensuring the development of accurate, biologically meaningful insights poised to advance medicine and biotechnology.
Citation: Giovanni Colonna. Protein biophysics: current limitations and prospects[J]. AIMS Biophysics, 2025, 12(3): 333-374. doi: 10.3934/biophy.2025018
This review critically examines the current landscape of protein biophysics, highlighting a significant epistemological divide emerging from the rapid advancement of predictive algorithms compared to the slower pace of experimental validation. While artificial intelligence models and inferential statistical methods can generate millions of in silico protein interactions, experimental biophysics remains limited by physiological and technical challenges, leading to a scarcity of confirmed, functional data. This imbalance creates an environment where hypotheses and large-scale interactome networks proliferate based on unverified interactions. This fosters a false sense of causal understanding, potential misdirection in therapeutic development, and overinterpreting omics datasets. The challenge becomes even more urgent with complex, multifactorial diseases such as cancer and viral infections. Recognizing this subtle but crucial disparity is essential for researchers in protein biophysics to navigate the evolving landscape effectively and to focus validation efforts that support meaningful scientific and clinical progress. The review emphasizes the critical need for rigorous experimental validation alongside rapid computational advances. It highlights key challenges, including complexities in protein folding, dynamics, membrane interactions, and quantum effects. It discusses emerging technologies such as AI-based structure prediction (e.g., AlphaFold), cryo-electron microscopy, and integrated biophysical approaches. The importance of multidisciplinary collaboration and orthogonal validation methods is underscored to enhance the reliability of protein interaction data. While technological advances promise to deepen our understanding of protein functions and their roles in health and disease, the review advocates for cautious integration of predictive models with meticulous experimental verification, ensuring the development of accurate, biologically meaningful insights poised to advance medicine and biotechnology.
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Giovanni Colonna. Protein biophysics: current limitations and prospects[J]. AIMS Biophysics, 2025, 12(3): 333-374. doi: 10.3934/biophy.2025018
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